import math
import folium
import numpy as np
import pandas as pd


import geopandas as gpd
from shapely.geometry import Point, Polygon, MultiPolygon
from folium.plugins import PolyLineTextPath
from geopy.distance import geodesic

import branca.colormap as cm

import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

import openmeteo_requests
import requests_cache
from retry_requests import retry

from xgboost import XGBRegressor
from sklearn.metrics import mean_absolute_error 
from sklearn.model_selection import GroupKFold, GridSearchCV, cross_val_score, cross_validate, train_test_split
from sklearn.compose import TransformedTargetRegressor

from IPython.display import display, HTML

%load_ext autoreload
%autoreload 2

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

pd.set_option('display.max_columns', None)

data = pd.read_csv('MVG_Rad_Fahrten_2023.csv', sep=';', decimal=',')
data

/var/folders/0j/hc4b4c153dzfbvkzcxfbtjv80000gn/T/ipykernel_1324/76297985.py:1: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.
  data = pd.read_csv('MVG_Rad_Fahrten_2023.csv', sep=';', decimal=',')

# column names contain trailing spaces
data.columns = data.columns.str.strip()
# drop the row column, but duplicate index values in a separate column. This will make some analysis more convenient
data.drop(columns=['Row'], inplace=True)
data.reset_index(names='trip_index', inplace=True)
data

# Convert STARTTIME and ENDTIME to datetime64. dtype of the rest of the columns is already correct
for col in ['STARTTIME', 'ENDTIME']:
    data[col] = pd.to_datetime(data[col])
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 710106 entries, 0 to 710105
Data columns (total 11 columns):
 #   Column               Non-Null Count   Dtype         
---  ------               --------------   -----         
 0   trip_index           710106 non-null  int64         
 1   STARTTIME            710106 non-null  datetime64[ns]
 2   ENDTIME              710106 non-null  datetime64[ns]
 3   STARTLAT             710106 non-null  float64       
 4   STARTLON             710106 non-null  float64       
 5   ENDLAT               710106 non-null  float64       
 6   ENDLON               710106 non-null  float64       
 7   RENTAL_IS_STATION    710106 non-null  object        
 8   RENTAL_STATION_NAME  710106 non-null  object        
 9   RETURN_IS_STATION    710106 non-null  object        
 10  RETURN_STATION_NAME  710106 non-null  object        
dtypes: datetime64[ns](2), float64(4), int64(1), object(4)
memory usage: 59.6+ MB

# Values in these columns also contain trailing spaces
for col in ['RENTAL_IS_STATION', 'RENTAL_STATION_NAME', 'RETURN_IS_STATION', 'RETURN_STATION_NAME']:
    data[col] = data[col].str.strip()

data.query('RENTAL_IS_STATION.isna()')['RENTAL_STATION_NAME'].value_counts(dropna=False)
data.query('RETURN_IS_STATION.isna()')['RETURN_STATION_NAME'].value_counts(dropna=False)

# RENTAL_IS_STATION is not reliable since it can be NA when the trip starts at a station.
# So let's just disregard RENTAL_IS_STATION and RETURN_IS_STATION and drop them. 
# Whether the trip started or finished at a station can be concluded from RENTAL_STATION_NAME and RETURN_STATION_NAME.

data.drop(columns=['RETURN_IS_STATION', 'RENTAL_IS_STATION'], inplace=True)

RENTAL_STATION_NAME
                                     51366
Sandstraße                             327
TUM Arcisstraße                        250
Hauptbahnhof Nord                      231
Olympiazentrum                         216
                                     ...  
Waldsiedlung Faistenhaar                 1
Ferd.-Kobell-Straße Haar                 1
Ottobrunner Straße Faistenhaar           1
S-Bahnhof Wächterhof                     1
Mallertshoffener Unterschleißheim        1
Name: count, Length: 315, dtype: int64

Series([], Name: count, dtype: int64)

# Let's make sure each station has the same coordinates in the entire dataset
all_stations_with_coord = pd.concat([data[['RENTAL_STATION_NAME', 'STARTLAT', 'STARTLON']].rename(columns={'RENTAL_STATION_NAME': 'station', 
                                                                                 'STARTLAT': 'lat', 
                                                                                 'STARTLON': 'lon'}), 
           data[['RETURN_STATION_NAME', 'ENDLAT', 'ENDLON']].rename(columns={'RETURN_STATION_NAME': 'station', 
                                                                                 'ENDLAT': 'lat', 
                                                                                 'ENDLON': 'lon'})], axis=0)
std_deviations = all_stations_with_coord.groupby('station').std()
std_deviations.head()
std_deviations.query('lat != 0 | lon != 0')
# Indeed, only for the. points that are not stations, the standard deviation of their coordinates is different from 0.
# Otherwise it is zero, so the coordinates of all stations are consistent.

data.info()
data.describe()
# We have no NA values, but what is clearly implausible is some coordinates in all coordinate columns. Some of the are equal to 0, and some are even negative

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 710106 entries, 0 to 710105
Data columns (total 9 columns):
 #   Column               Non-Null Count   Dtype         
---  ------               --------------   -----         
 0   trip_index           710106 non-null  int64         
 1   STARTTIME            710106 non-null  datetime64[ns]
 2   ENDTIME              710106 non-null  datetime64[ns]
 3   STARTLAT             710106 non-null  float64       
 4   STARTLON             710106 non-null  float64       
 5   ENDLAT               710106 non-null  float64       
 6   ENDLON               710106 non-null  float64       
 7   RENTAL_STATION_NAME  710106 non-null  object        
 8   RETURN_STATION_NAME  710106 non-null  object        
dtypes: datetime64[ns](2), float64(4), int64(1), object(2)
memory usage: 48.8+ MB

# We see that within 1.5% and 99%- quantiles the coordinates are roughly plausible, at least up to their integer parts.

data[['STARTLAT',	'STARTLON',	'ENDLAT', 'ENDLON']].quantile(0.015)
data[['STARTLAT',	'STARTLON',	'ENDLAT', 'ENDLON']].quantile(0.99)

STARTLAT    48.03886
STARTLON    11.46081
ENDLAT      48.03864
ENDLON      11.45315
Name: 0.015, dtype: float64

STARTLAT    48.24959
STARTLON    11.73105
ENDLAT      48.24959
ENDLON      11.72308
Name: 0.99, dtype: float64

ranges = {'STARTLAT': (47.9, 48.32), 'ENDLAT': (47.9, 48.32), 'STARTLON': (11.2, 12), 'ENDLON': (11.2, 12)}

query_str = ' & '.join([f'{col} >= {low} & {col} <= {high}' for col, (low, high) in ranges.items()])
data_filtered = data.query(query_str).copy()
data_filtered
data_filtered[['STARTLAT',	'STARTLON',	'ENDLAT',	'ENDLON']].apply([min, max])

# First, get coordinates for all stations, n_trips will denote the number of trips that started at that station
station_coordinates = data_filtered.query('RENTAL_STATION_NAME != ""')[['RENTAL_STATION_NAME', 'STARTLAT', 'STARTLON']].groupby('RENTAL_STATION_NAME')\
                          .agg(latitude=pd.NamedAgg(column="STARTLAT", aggfunc="first"),
                              longitude=pd.NamedAgg(column="STARTLON", aggfunc="first"),
                              n_trips=pd.NamedAgg(column="STARTLAT", aggfunc="count"))
station_coordinates  = station_coordinates.reset_index().rename(columns={'RENTAL_STATION_NAME': 'STATION_NAME'})
station_coordinates

# Do two joins on coordinates and copy the station name to  RENTAL_STATION_NAME and RETURN_STATION_NAME
#  Now we will have some NAs in new columns RENTAL_STATION_NAME	and RETURN_STATION_NAME (previously they were empty strings).
data_filtered = data_filtered.merge(
    station_coordinates[['STATION_NAME', 'latitude', 'longitude']],
    left_on=['STARTLAT', 'STARTLON'], right_on=['latitude', 'longitude'],
    how='left'
)
data_filtered['RENTAL_STATION_NAME'] = data_filtered['STATION_NAME']
data_filtered.drop(columns=['STATION_NAME', 'latitude', 'longitude'], inplace=True)

data_filtered = data_filtered.merge(
    station_coordinates[['STATION_NAME', 'latitude', 'longitude']],
    left_on=['ENDLAT', 'ENDLON'], right_on=['latitude', 'longitude'],
    how='left'
)
data_filtered['RETURN_STATION_NAME'] = data_filtered['STATION_NAME']
data_filtered.drop(columns=['STATION_NAME', 'latitude', 'longitude'], inplace=True)

data_filtered

# Resulting value counts for RENTAL_STATION_NAME and RETURN_STATION_NAME: as will be discussed later, most trips start and end outside stations
data_filtered['RENTAL_STATION_NAME'].value_counts(dropna=False)
data_filtered['RETURN_STATION_NAME'].value_counts(dropna=False)

RENTAL_STATION_NAME
NaN                         538323
Sandstraße                    3476
TUM Arcisstraße               2623
Hauptbahnhof Nord             2601
Münchner Freiheit             2402
                             ...  
Tölzer Straße Otterloh          17
Domagkstraße Kirchheim          12
Am Sportpark Feldkirchen        11
Waldsiedlung Faistenhaar         9
Bogenstraße Waldbrunn            4
Name: count, Length: 332, dtype: int64

RETURN_STATION_NAME
NaN                         571970
Sandstraße                    2966
Hauptbahnhof Nord             2456
TUM Arcisstraße               2134
Olympiazentrum                2091
                             ...  
Am Sportpark Feldkirchen        13
Ferd.-Kobell-Straße Haar        12
Parkplatz Grünwald              11
Waldsiedlung Faistenhaar         7
Bogenstraße Waldbrunn            3
Name: count, Length: 332, dtype: int64

def plot_location_counts(data, latitude_column, longitude_column, marker_size, title, **kwargs):
    #  Aggregate the data by location
    agg_data = data[[latitude_column, longitude_column]].groupby([latitude_column, longitude_column]).size().reset_index(name='count')

    # Normalize the count to scale marker sizes
    max_count = agg_data['count'].max()
    agg_data['size'] = agg_data['count'] / max_count * marker_size  


    #  Create a Folium map
    m = folium.Map(location=[data[latitude_column].mean(), data[longitude_column].mean()], zoom_start=12)

    # Add markers to the map
    for i, row in agg_data.sample(**kwargs).iterrows():
        _ = folium.CircleMarker(
            location=[row[latitude_column], row[longitude_column]],
            radius=row['size'], 
            color='blue',
            fill=True,
            fill_color='blue',
            fill_opacity=0.6,
            popup=f"Count: {row['count']}"
        ).add_to(m);

    display(HTML(title))
    display(m)

%%time
sample_size = 20000
plot_location_counts(data_filtered, 'STARTLAT', 'STARTLON', 50, "<h3>Trip Start Locations (marker size reflects the number of trip starts)</h3>", n=sample_size)
plot_location_counts(data_filtered, 'ENDLAT', 'ENDLON', 50, "<h3>Trip End Locations (marker size reflects the number of trip ends)</h3>", n=sample_size)

CPU times: user 46.1 s, sys: 1.23 s, total: 47.3 s
Wall time: 48.8 s

%%time


# Convert data to a GeoDataFrame
starts_geometry = [Point(xy) for xy in zip(data_filtered['STARTLON'], data_filtered['STARTLAT'])]
finishes_geometry = [Point(xy) for xy in zip(data_filtered['ENDLON'], data_filtered['ENDLAT'])]
starts_geo_df = gpd.GeoDataFrame(data_filtered[['STARTLON', 'STARTLAT']], geometry=starts_geometry, crs='EPSG:4326')
finishes_geo_df = gpd.GeoDataFrame(data_filtered[['ENDLON', 'ENDLAT']], geometry=finishes_geometry, crs='EPSG:4326')

# Create the spatial grid (square grid in this case)
s_bounds = starts_geo_df.total_bounds
e_bounds = finishes_geo_df.total_bounds

xmin = min(s_bounds[0], e_bounds[0])
ymin = min(s_bounds[1], e_bounds[1])
xmax = max(s_bounds[2], e_bounds[2])
ymax = max(s_bounds[3], e_bounds[3])

xmin, ymin, xmax, ymax 
grid_size = 0.01 

# Create grid squares
x_coords = list(range(int(xmin/grid_size), int(xmax/grid_size) + 1))
y_coords = list(range(int(ymin/grid_size), int(ymax/grid_size) + 1))

polygons = [Polygon([(x*grid_size, y*grid_size), ((x+1)*grid_size, y*grid_size),
                     ((x+1)*grid_size, (y+1)*grid_size), (x*grid_size, (y+1)*grid_size)])
            for x in x_coords for y in y_coords]

grid = gpd.GeoDataFrame({'geometry': polygons}, crs='EPSG:4326')



# Spatial join to aggregate points into the grid
joined_starts = gpd.sjoin(starts_geo_df, grid, how='left', predicate='within')
joined_finishes = gpd.sjoin(finishes_geo_df, grid, how='left', predicate='within')

CPU times: user 17.7 s, sys: 276 ms, total: 18 s
Wall time: 18.9 s

grid['start_and_finish_count'] = pd.concat([joined_starts.rename(columns={'STARTLON': 'LON', 'STARTLAT': 'LAT'}),
                                joined_finishes.rename(columns={'ENDLON': 'LON', 'ENDLAT': 'LAT'})]).groupby('index_right').size()
grid['start_count'] = joined_starts.groupby('index_right').size()
grid['finish_count'] = joined_finishes.groupby('index_right').size()
                    
grid['start_and_finish_count'] = grid['start_and_finish_count'].fillna(0)
grid['start_count'] = grid['start_count'].fillna(0)
grid['finish_count'] = grid['finish_count'].fillna(0)

def plot_grid(grid, joined, trip_count_col, title):

    grid = grid.copy()

    # Plot the results on a Folium map
    m = folium.Map(location=[joined['geometry'].y.mean(), joined['geometry'].x.mean()], zoom_start=12)
    
    # Normalize the trip count for coloring
    max_count = grid[trip_count_col].max()
    grid['color'] = grid[trip_count_col] / max_count * 100  # Scale color intensity by trip count

    # Add bins to the map
    for cell_index, row in grid.iterrows():
        if row[trip_count_col] > 0:  # Only plot bins with at least one trip start
            folium.GeoJson(row['geometry'].__geo_interface__,
                           style_function=lambda x, count=row[trip_count_col]: {
                            'fillColor': f'#{int(count * 255 / max_count):02x}{int(count * 255 / max_count):02x}{int(count * 255 / max_count):02x}',
                               'color': 'black',
                               'weight': 1,
                               'fillOpacity': 0.6
                       },
                           tooltip=f'Polygon {cell_index},\nTrip Count: {int(row[trip_count_col])}').add_to(m)

    display(HTML(title))
    display(m)

plot_grid(grid, joined_starts, 'start_count', "<h3>Trip Start Density (lighter colour reflects higher density)</h3>")
plot_grid(grid, joined_finishes, 'finish_count', "<h3>Trip End Density (lighter colour reflects higher density)</h3>")

joined_starts.shape
joined_finishes.shape

(694657, 4)

(694657, 4)

# The whole number of trips: 
data_filtered.shape[0], joined_starts.shape[0], joined_finishes.shape[0]

# The number of trips that started on the grid: they have no index_right, which corresponds to the index in the grid dataframe
(f'started on the grid: {joined_starts['index_right'].isna().sum()}')

# The number of trips that ended on the grid: they have no index_right, which corresponds to the index in the grid dataframe
(f'ended on the grid: {joined_finishes['index_right'].isna().sum()}')

#  The total number of trip starts and ends in the grid table where we now have trip_count for each polygon
(f'total starts plotted: {grid["start_count"].sum()}')
(f'total ends plotted: {grid["finish_count"].sum()}')

# The diffence between the total trip count in data_filtered and in the grid dataframe corresponds exactly to the number of trips started or ended on the grid
data_filtered.shape[0] - grid['start_count'].sum()
data_filtered.shape[0] - grid['finish_count'].sum()

(694657, 694657, 694657)

'started on the grid: 888'

'ended on the grid: 952'

'total starts plotted: 693769.0'

'total ends plotted: 693705.0'

np.float64(888.0)

np.float64(952.0)

joined_starts_finishes = joined_starts.merge(joined_finishes, left_index=True, right_index=True, suffixes=('_s', '_f'))
joined_starts_finishes

starts_finishes_crosstab = pd.crosstab(
            joined_starts_finishes['index_right_s'], 
            joined_starts_finishes['index_right_f'],
            margins=False
)
starts_finishes_crosstab

def jitter_line(src: Point, dst: Point, magnitude: float = 0.0002, reverse=False):
    """
    Offsets a line between src and dst by a small perpendicular jitter.
    """
    dx = dst.x - src.x
    dy = dst.y - src.y
    length = math.hypot(dx, dy)
    if length == 0:
        return [(src.y, src.x), (dst.y, dst.x)]  # avoid divide by zero

    # Normalize perpendicular direction
    ux = -dy / length
    uy = dx / length

    jitter_x = ux * (magnitude + np.random.normal(loc=0, scale=magnitude/2))
    jitter_y = uy * (magnitude + np.random.normal(loc=0, scale=magnitude/2))

    jittered_src = (src.y + jitter_y, src.x + jitter_x)  # (lat, lon)
    jittered_dst = (dst.y + jitter_y, dst.x + jitter_x)

    return [jittered_src, jittered_dst]

cmap = cm.LinearColormap(['lightblue','darkblue'], vmin=0, vmax=1)

# compute centroids
centroids = grid.geometry.centroid

# centre map
mean_x, mean_y = centroids.x.mean(), centroids.y.mean()
m = folium.Map(location=[mean_y, mean_x], zoom_start=12);

# color scale
min_c = starts_finishes_crosstab.values[starts_finishes_crosstab.values > 0].min()
max_c = starts_finishes_crosstab.values.max()

for _, row in grid.iterrows():
    if row['start_and_finish_count'] > 0:  # Only plot bins with at least one trip start
        _ = folium.GeoJson(row['geometry'].__geo_interface__,
                           style_function=lambda x : {
                            'fillColor': '#d3d3d3',
                               'color': 'black',
                               'weight': 1,
                               'fillOpacity': 0.6
                       },
                           ).add_to(m)


# draw lines & arrows
for src, row in starts_finishes_crosstab.iterrows():
    for dst, cnt in row.items():
        if cnt > 1000:
            src_pt, dst_pt = centroids.loc[src], centroids.loc[dst]
            coords = jitter_line(src_pt, dst_pt, magnitude=0.002, reverse=src > dst)
            weight =  (cnt - min_c) / (max_c - min_c) * 10
            color = 'blue'

            line = folium.PolyLine(locations=coords, weight=weight,
                                  color=color, opacity=0.9,
                                  tooltip=f'Trips: {cnt}').add_to(m);

            _ = PolyLineTextPath(line, '➤', repeat=False, offset=6,
                                     attributes={'fill': color,
                                                 'font-weight': 'bold',
                                                 'font-size': '18'}).add_to(m);


display(HTML("<h3>Major Trip Flows (marker and line thickness reflects the number of trips)</h3>"))
display(m)

/var/folders/0j/hc4b4c153dzfbvkzcxfbtjv80000gn/T/ipykernel_1324/3679063521.py:4: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  centroids = grid.geometry.centroid

data_filtered['trip_duration_minutes'] = (data_filtered['ENDTIME'] - data_filtered['STARTTIME']).dt.total_seconds() / 60

fig, axes = plt.subplots(1, 2, figsize=(12, 6))

sns.boxplot(x=data_filtered['trip_duration_minutes'], ax=axes[0])
axes[0].set_title('All trip durations')

sns.boxplot(x=data_filtered['trip_duration_minutes'], ax=axes[1])
axes[1].set_xlim(-100, 100)
axes[1].set_title('All trip durations below 60 minutes')

plt.tight_layout();

fig, axes = plt.subplots(1, 2, figsize=(12, 6))  # 1 row, 2 columns

# First plot
sns.boxplot(data_filtered['trip_duration_minutes'], ax=axes[0])
axes[0].set_title('All trip durations')

# Second plot
sns.boxplot(data_filtered['trip_duration_minutes'], ax=axes[1])
axes[1].set_ylim(-100, 100)
axes[1].set_title('All trip durations below 60 minutes')

plt.tight_layout();

data_filtered[['trip_duration_minutes']].describe()

data_filtered['trip_duration_minutes'].quantile(0.95)

np.float64(51.0)

data_filtered[data_filtered['trip_duration_minutes'] < 0]
data_filtered[data_filtered['trip_duration_minutes'] < 0].shape

(14, 10)

sns.histplot(data_filtered.query('trip_duration_minutes < 500')['trip_duration_minutes']);
plt.title("Distribution of Trip Durations Below 500 Minutes");

data_filtered.query('trip_duration_minutes > 1000').shape[0] / data_filtered.shape[0]

0.0034304700017418668

data_filtered['trip_distance_km'] = data_filtered.apply(lambda row: geodesic((row['STARTLAT'], row['STARTLON']),
                                                           (row['ENDLAT'], row['ENDLON'])).km, axis=1);

data_filtered[['trip_distance_km']].describe()

sns.boxplot(data_filtered['trip_distance_km']);
plt.title('Trip Distances Boxplot');

sns.histplot(data_filtered['trip_distance_km']);
plt.title('Trip Distances Histogram');

data_filtered.query('trip_distance_km < .5').shape[0] / data_filtered.shape[0] 
data_filtered.query('trip_distance_km < 0.01').shape[0] / data_filtered.shape[0] 
data_filtered.query('trip_distance_km == 0').shape[0] / data_filtered.shape[0]

0.15562356673869263

0.04736006403160121

0.03454078775568374

data_filtered[['trip_distance_km', 'trip_duration_minutes']].corr()

monthly_aggregated_starts = data_filtered.resample('1MS', on='STARTTIME')['STARTTIME'].count()
monthly_aggregated_starts.plot(figsize=(15,6), title="Trip Starts per Month");

daily_aggregated_starts = data_filtered.resample('1D', on='STARTTIME')['STARTTIME'].count()
daily_aggregated_starts.plot(figsize=(15,6),  title="Trip Starts per Day");

hourly_aggregated_starts = data_filtered.resample('1h', on='STARTTIME')['STARTTIME'].count()
hourly_aggregated_starts.plot(figsize=(15,6),  title="Trip Starts per Hour");

hourly_aggregated_starts['2023-08-19' : '2023-09-02'].plot(figsize=(15,6),  title="Trip Starts per Hour (Zoomed In)");

average_starts_by_weekday = daily_aggregated_starts.groupby(daily_aggregated_starts.index.weekday).mean()
average_starts_by_weekday.plot(figsize=(15,6), ylim=(0, 2300),  title="Average Starts Count per Weekday");

average_starts_by_hour_of_the_day = hourly_aggregated_starts.groupby(hourly_aggregated_starts.index.hour).mean()
average_starts_by_hour_of_the_day.plot(figsize=(15,6),  title="Average Starts Count per Hour of the Day");

data_filtered['RENTAL_STATION_NAME'].value_counts(dropna=False).head(20)
# NaN corresponds to trips that started outside stations

RENTAL_STATION_NAME
NaN                               538323
Sandstraße                          3476
TUM Arcisstraße                     2623
Hauptbahnhof Nord                   2601
Münchner Freiheit                   2402
Universität                         2377
Olympiazentrum                      2370
Hackerbrücke                        2157
Königinstraße                       1971
Pasing                              1797
Rotkreuzplatz                       1753
Maillingerstraße                    1745
Technische Universität München      1742
Goetheplatz (Nord)                  1669
Brudermühlstraße                    1635
Türkenstraße                        1606
Hohenzollernplatz                   1586
Josephsplatz                        1505
Klinikum Großhadern                 1497
Nordbad                             1438
Name: count, dtype: int64

data_filtered[['STARTLAT',	'STARTLON', 'STARTTIME']]\
    .groupby(['STARTLAT',	'STARTLON']).count().sort_values('STARTTIME', ascending=False).head(10)

data_filtered['RETURN_STATION_NAME'].value_counts(dropna=False).head(20)
data_filtered[['ENDLAT',	'ENDLON', 'ENDTIME']]\
    .groupby(['ENDLAT',	'ENDLON']).count().sort_values('ENDTIME', ascending=False).head(10)

RETURN_STATION_NAME
NaN                               571970
Sandstraße                          2966
Hauptbahnhof Nord                   2456
TUM Arcisstraße                     2134
Olympiazentrum                      2091
Universität                         1976
Pasing                              1826
Münchner Freiheit                   1783
Hackerbrücke                        1701
Königinstraße                       1639
Rotkreuzplatz                       1518
Maillingerstraße                    1388
Technische Universität München      1382
Brudermühlstraße                    1365
Josephsplatz                        1324
Türkenstraße                        1324
Klinikum Großhadern                 1319
Goetheplatz (Nord)                  1269
Nordbad                             1202
Maibaumplatz Garching               1083
Name: count, dtype: int64

plot_location_counts(data_filtered.query('RENTAL_STATION_NAME.notna()'), 'STARTLAT', 'STARTLON', 10, "<h3>Trip Starts at Stations (marker size reflects the number of trip starts)</h3>", frac=1)
plot_location_counts(data_filtered.query('RETURN_STATION_NAME.notna()'), 'ENDLAT', 'ENDLON', 10, "<h3>Trip Ends at Stations (marker size reflects the number of trip starts)</h3>", frac=1)

def plot_trips_for_stations(df, station_name, frequency):
    
    starts_for_station = df.query('RENTAL_STATION_NAME == @station_name').set_index('STARTTIME').resample(frequency).count()['ENDTIME']
    finishes_for_station = df.query('RETURN_STATION_NAME == @station_name').set_index('ENDTIME').resample(frequency).count()['STARTTIME']
    starts_and_finishes = pd.concat([starts_for_station, finishes_for_station], axis=1)
    n_bikes_delta_for_station = starts_and_finishes['ENDTIME'].fillna(0) - starts_and_finishes['STARTTIME'].fillna(0)

    fig = plt.figure();
    plt.title(f'Number of trip starts for {station_name}')
    ax = starts_for_station.plot(figsize=(15, 6));
    print(starts_for_station.value_counts(dropna=False))


    fig = plt.figure();
    plt.title(f'Number of trip ends for {station_name}')
    ax = finishes_for_station.plot(figsize=(15, 6));
    print(finishes_for_station.value_counts(dropna=False))

    fig = plt.figure();
    plt.title(f'Difference between number of starts and number of finishes for {station_name}')
    ax = n_bikes_delta_for_station.plot(figsize=(15, 6));
    print(n_bikes_delta_for_station.value_counts(dropna=False))

plot_trips_for_stations(data_filtered, 'Hauptbahnhof Nord', '20Min')

ENDTIME
0    24030
1     1738
2      281
3       67
4       17
5        5
7        1
Name: count, dtype: int64
STARTTIME
0    24078
1     1825
2      239
3       39
4        9
Name: count, dtype: int64
 0.0    22569
-1.0     1551
 1.0     1541
 2.0      239
-2.0      190
 3.0       45
-3.0       34
 4.0       10
-4.0        8
 5.0        2
 6.0        1
Name: count, dtype: int64

plot_trips_for_stations(data_filtered, 'Messestadt West', '20Min')

ENDTIME
0    25345
1      369
2       48
3       13
4        2
5        1
Name: count, dtype: int64
STARTTIME
0    25314
1      409
2       49
3        3
5        1
4        1
Name: count, dtype: int64
 0.0    24980
-1.0      360
 1.0      348
-2.0       41
 2.0       39
 3.0        6
-3.0        3
 4.0        2
-5.0        1
-4.0        1
Name: count, dtype: int64

# let us construct a dataframe where each row is a trip start or a trip end "event".

data_filtered['start_coord_zipped'] = list(zip(data_filtered['STARTLAT'], data_filtered['STARTLON']))
data_filtered['end_coord_zipped'] = list(zip(data_filtered['ENDLAT'], data_filtered['ENDLON']))

start_events = pd.DataFrame({
    'trip_index':  data_filtered['trip_index'],
    'timestamp':       data_filtered['STARTTIME'],
    'location':      data_filtered['start_coord_zipped'],
    'kind':     'start'
})
end_events = pd.DataFrame({
    'trip_index':  data_filtered['trip_index'],
    'timestamp':       data_filtered['ENDTIME'],
    'location':      data_filtered['end_coord_zipped'],
    'kind':     'end'
})
start_and_end_events = pd.concat([start_events, end_events], ignore_index=True)

# sort all events by timestamp and by kind: first ends, then starts
start_and_end_events.sort_values(['timestamp', 'kind'], inplace=True)

start_and_end_events

start_and_end_events['is_start'] = (start_and_end_events['kind'] == 'start').astype(int)
start_and_end_events['is_end']   = (start_and_end_events['kind'] == 'end').astype(int)

# compute the number of starts and ends “so far, up to but NOT including this row” counts within each location group (i.e. at each location)
# However, potentially this introduces some arbitrariness: if several trips start or end at the same location and at the same time, whether they will be taken into account in a cumulated sum, depends on the order, in which they appear in the 
# dataframe. We will deal with this later. 
start_and_end_events_by_location = start_and_end_events.groupby('location')
start_and_end_events['n_starts_before'] = start_and_end_events_by_location['is_start'].cumsum() - start_and_end_events['is_start']
start_and_end_events['n_ends_before']   = start_and_end_events_by_location['is_end'].cumsum()   - start_and_end_events['is_end']

# drop the indicators 
start_and_end_events = start_and_end_events.drop(['is_start','is_end'], axis=1)
start_and_end_events

# Difference between the number of ends before the current trip (at this location) and the number of starts before the current trip (at this location) is equal to the assumed number of bikes 
# at this location shortly before this trip
start_and_end_events['assumed_bike_number_at_timestamp'] = start_and_end_events['n_ends_before'] - start_and_end_events['n_starts_before']
start_and_end_events

%%time
# Count occurrences of each (timestamp, coordinates, type) combination
grouped_start_and_end_events = start_and_end_events.groupby(['timestamp', 'location', 'kind']).size().reset_index(name='count')

# Filter to only combinations that appear 2+ times (duplicates)
ambigouos_time_location_combinations = grouped_start_and_end_events[grouped_start_and_end_events['count'] >= 2][['timestamp', 'location']].drop_duplicates()

# Mark duplicates as True for efficient join
ambigouos_time_location_combinations['is_ambiguous_ambigouos_time_location_combination'] = True

# Join back to original dataframe on timestamp and coordinates, this will mark both start and end rows with matching timestamp+coordinates
start_and_end_events = start_and_end_events.merge(
    ambigouos_time_location_combinations, 
    on=['timestamp', 'location'], 
    how='left'
)

# Set special value where duplicates were found
start_and_end_events.loc[start_and_end_events['is_ambiguous_ambigouos_time_location_combination'] == True, 'assumed_bike_number_at_timestamp'] = 9999

# Drop the helper column
start_and_end_events = start_and_end_events.drop('is_ambiguous_ambigouos_time_location_combination', axis=1)

CPU times: user 3.37 s, sys: 271 ms, total: 3.64 s
Wall time: 3.68 s

data_filtered = data_filtered.merge(start_and_end_events.query('kind == "start"')[['trip_index', 'assumed_bike_number_at_timestamp']], on='trip_index', how='left')
data_filtered.rename(columns={'assumed_bike_number_at_timestamp': 'assumed_bike_number_at_starttime'}, inplace=True)
data_filtered = data_filtered.merge(start_and_end_events.query('kind == "end"')[['trip_index', 'assumed_bike_number_at_timestamp']], on='trip_index', how='left')
data_filtered.rename(columns={'assumed_bike_number_at_timestamp': 'assumed_bike_number_at_endtime'}, inplace=True)
data_filtered

data_filtered['assumed_bike_number_at_starttime'].value_counts(dropna=False)
data_filtered['assumed_bike_number_at_endtime'].value_counts(dropna=False)

assumed_bike_number_at_starttime
 1       524919
 0        11145
 9999      9143
 2         5349
-1         1739
          ...  
-586          1
 82           1
-562          1
-565          1
-623          1
Name: count, Length: 725, dtype: int64

assumed_bike_number_at_endtime
 0       566535
 9999      8272
 1         5808
-1         2474
-2         1463
          ...  
-542          1
-515          1
-588          1
-585          1
-586          1
Name: count, Length: 706, dtype: int64

%%time
merged = pd.merge(data_filtered, data_filtered, left_on='end_coord_zipped', right_on='start_coord_zipped', suffixes=('_current', '_next'))

CPU times: user 1min 47s, sys: 2min 4s, total: 3min 51s
Wall time: 5min 13s

merged = merged[(merged['STARTTIME_next'] >= merged['ENDTIME_current'])  & (merged['trip_index_current'] != merged['trip_index_next'])]
merged

df_next_trip = merged.loc[merged.groupby('trip_index_current')['STARTTIME_next'].idxmin()]
df_next_trip

map_dict_next_trip = df_next_trip[['trip_index_current', 'trip_index_next']].set_index('trip_index_current').to_dict()['trip_index_next']

data_filtered['NEXTTRIP'] = data_filtered['trip_index'].map(map_dict_next_trip)
data_filtered

map_dict_previous_trip = df_next_trip[['trip_index_current', 'trip_index_next']].set_index('trip_index_next').to_dict()['trip_index_current']
data_filtered['PREVIOUSTRIP'] = data_filtered['trip_index'].map(map_dict_previous_trip)
data_filtered

data_filtered.loc[data_filtered['RENTAL_STATION_NAME'].notna(), 'PREVIOUSTRIP'] = np.nan
data_filtered.loc[data_filtered['RETURN_STATION_NAME'].notna(), 'NEXTTRIP'] = np.nan

data_filtered.loc[data_filtered['assumed_bike_number_at_starttime'] != 1, 'PREVIOUSTRIP'] = np.nan
data_filtered.loc[data_filtered['assumed_bike_number_at_endtime'] != 0, 'NEXTTRIP'] = np.nan

%%time
trip_chains_list = []
data_filtered['trip_index_2'] = data_filtered['trip_index']
trip_index_to_row = data_filtered.set_index('trip_index_2').to_dict(orient='index')
trip_index_to_next = data_filtered.set_index('trip_index')['NEXTTRIP'].to_dict()


for trip_start in data_filtered[data_filtered['PREVIOUSTRIP'].isna()]['trip_index']:
    first_trip_start = trip_start
    lst = []
    trip_segment_number = 1
    while True:
        row_data = trip_index_to_row.get(trip_start)
        if (row_data is None) or ((row_data['assumed_bike_number_at_starttime'] != 1) and (trip_segment_number > 1)):
            break
        lst.append(row_data)
        next_trip = trip_index_to_next.get(trip_start)
        if pd.isna(next_trip):
            break
        trip_start = next_trip
        trip_segment_number += 1

    trip_chain = pd.DataFrame(lst)
    trip_chain['trip_chain_index'] = first_trip_start
    trip_chains_list.append(trip_chain)

CPU times: user 3min 12s, sys: 4.82 s, total: 3min 17s
Wall time: 3min 21s

all_trip_chains = pd.concat(trip_chains_list, ignore_index=True)
all_trip_chains.shape

(694548, 18)

all_trip_chains['trip_index'].value_counts(dropna=False)

trip_index
710105    1
0         1
1         1
3464      1
710062    1
         ..
4988      1
4086      1
4041      1
3599      1
3548      1
Name: count, Length: 694548, dtype: int64

# The longest trip chain we could reconstruct consists of 65 trips
all_trip_chains.groupby('trip_chain_index')['trip_index'].count().sort_values(ascending=False)

trip_chain_index
255855    65
502264    64
38459     63
565133    61
520450    60
          ..
709932     1
709929     1
709928     1
709927     1
145791     1
Name: trip_index, Length: 171597, dtype: int64

def plot_trip_chains(trip_chain_index):
    
    m = folium.Map(location=[data_filtered['STARTLAT'].mean(), data_filtered['STARTLON'].mean()], zoom_start=12)
    
    for _, row in all_trip_chains.query('trip_chain_index == @trip_chain_index').iterrows():
        q = folium.PolyLine(locations=[(row['STARTLAT'], row['STARTLON']),
                                   (row['ENDLAT'], row['ENDLON'])],
                        color='red', tooltip=row['trip_index']).add_to(m);
    display(HTML(f"<h3>Trip chain {trip_chain_index}</h3>"))
    display(m)
    
    sns.histplot(all_trip_chains.query('trip_chain_index == @trip_chain_index')['trip_duration_minutes'])
    plt.title(f'Distribution of Trip Duration for Trip Chain {trip_chain_index}');
    display(all_trip_chains.query('trip_chain_index == @trip_chain_index'))

plot_trip_chains(255855)
# the longest trip chain we could reconsrtuct

plot_trip_chains(629025)

# this trip chain is remarkable because it seems that the same bike was for at least half a month for commuting among (mainly) 4 locations.
plot_trip_chains(685353)

all_trip_chains_next_trip_start = all_trip_chains.merge(all_trip_chains[['trip_index', 'STARTTIME', 'assumed_bike_number_at_starttime']], how='left', left_on='NEXTTRIP', right_on='trip_index', suffixes=('_current', '_next'))
all_trip_chains_next_trip_start.loc[all_trip_chains_next_trip_start['assumed_bike_number_at_starttime_next'] != 1, 'STARTTIME_next'] = pd.NaT
all_trip_chains_next_trip_start.drop(columns=['trip_index_next'], inplace=True)
all_trip_chains_next_trip_start.rename(columns={'STARTTIME_next': 'next_trip_start'}, inplace=True)

all_trip_chains_next_trip_start['idle_time_to_next_trip'] = all_trip_chains_next_trip_start['next_trip_start'].sub(all_trip_chains_next_trip_start['ENDTIME'])

# let's convert seconds to days
all_trip_chains_next_trip_start[ 'idle_time_to_next_trip_days'] = \
    all_trip_chains_next_trip_start['idle_time_to_next_trip'].dt.total_seconds() / 60 / 60 / 24

fig, axes = plt.subplots(1, 2, figsize=(12, 6))  # 1 row, 2 columns

sns.boxplot(all_trip_chains_next_trip_start['idle_time_to_next_trip_days'], ax=axes[0])
axes[0].set_title('All idle time distribution')

sns.boxplot(all_trip_chains_next_trip_start['idle_time_to_next_trip_days'], ax=axes[1])
axes[1].set_ylim(0, 10)
axes[1].set_title('Idle time below 10 days')

plt.tight_layout();

# for approx. 24% of all trips  the idle time can be estimated realiably
all_trip_chains_next_trip_start['idle_time_to_next_trip_days'].isna().mean()

np.float64(0.247062837989599)

all_trip_chains_next_trip_start[['idle_time_to_next_trip_days']].describe()

sns.histplot(all_trip_chains_next_trip_start.query('idle_time_to_next_trip_days < 10')['idle_time_to_next_trip_days']);
plt.title('Idle time below 10 days');

all_trip_chains_next_trip_start.query('idle_time_to_next_trip_days >= 10').shape[0] / all_trip_chains_next_trip_start['idle_time_to_next_trip_days'].dropna().shape[0]

0.01055548225359546

%%time
# Aggregate the data by start location
long_idle_time = all_trip_chains_next_trip_start.query('idle_time_to_next_trip_days > 30').copy()

# Normalize the count to scale marker sizes (optional)
max_time = long_idle_time['idle_time_to_next_trip_days'].max()
long_idle_time['size'] = long_idle_time['idle_time_to_next_trip_days'] / max_time * 10 
#agg_data


# Create a Folium map
m = folium.Map(location=[data_filtered['STARTLAT'].mean(), data_filtered['STARTLON'].mean()], zoom_start=12)

# Add markers to the map
for i, row in long_idle_time.iterrows():
    _ = folium.CircleMarker(
        location=[row['ENDLAT'], row['ENDLON']],
        radius=row['size'], 
        color='blue',
        fill=True,
        fill_color='blue',
        fill_opacity=0.6,
        popup=f"Trip {row['trip_index_current']}, {row['idle_time_to_next_trip_days']} days"
    ).add_to(m);

display(HTML("<h3>Locations With Exceptionally Long Bike Idle Times</h3>"))
m

CPU times: user 93.8 ms, sys: 5.09 ms, total: 98.9 ms
Wall time: 101 ms

#   One example of an especially long idle time
ind = 29908
all_trip_chains_next_trip_start.query('trip_index_current == @ind')
start_and_end_events.query('trip_index == [@ind]')
start_and_end_events[start_and_end_events['location'] == (48.12469, 11.59479)]

def get_weather_data(start_date, end_date, lat, lon):
    # Setup the Open-Meteo API client with cache and retry on error
    cache_session = requests_cache.CachedSession('.cache', expire_after = -1)
    retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
    openmeteo = openmeteo_requests.Client(session = retry_session)
    
    # Make sure all required weather variables are listed here
    # The order of variables in hourly or daily is important to assign them correctly below
    url = "https://archive-api.open-meteo.com/v1/archive"
    params = {
    	"latitude": lat,
    	"longitude": lon,
    	"start_date": start_date,
    	"end_date": end_date,
    	"hourly": ["temperature_2m", "precipitation", "wind_speed_10m"],
    	"timezone": "Europe/Berlin"
    }
    responses = openmeteo.weather_api(url, params=params)
    
    response = responses[0]
    print(f"Coordinates {response.Latitude()}°N {response.Longitude()}°E")
    print(f"Elevation {response.Elevation()} m asl")
    print(f"Timezone {response.Timezone()} {response.TimezoneAbbreviation()}")
    print(f"Timezone difference to GMT+0 {response.UtcOffsetSeconds()} s")
    
    # Process hourly data. The order of variables needs to be the same as requested.
    hourly = response.Hourly()
    hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy()
    hourly_precipitation = hourly.Variables(1).ValuesAsNumpy()
    hourly_wind_speed_10m = hourly.Variables(2).ValuesAsNumpy()
    
    hourly_data = {"date": pd.date_range(
    	start = pd.to_datetime(hourly.Time(), unit="s"),
    	end = pd.to_datetime(hourly.TimeEnd(), unit="s"),
    	freq = pd.Timedelta(seconds=hourly.Interval()),
    	inclusive = "left"
    )}
    hourly_data["temperature_2m"] = hourly_temperature_2m
    hourly_data["precipitation"] = hourly_precipitation
    hourly_data["wind_speed_10m"] = hourly_wind_speed_10m
    
    hourly_dataframe = pd.DataFrame(data=hourly_data)
    return hourly_dataframe

# we retrieve the weather data for 2023 using the code suggested on https://open-meteo.com
weather_2023_df = get_weather_data("2023-01-01", "2024-01-01", 48.1374, 11.5755)
weather_2023_df

Coordinates 48.1195068359375°N 11.550000190734863°E
Elevation 524.0 m asl
Timezone b'Europe/Berlin' b'GMT+2'
Timezone difference to GMT+0 7200 s

def prepare_data_for_modelling(grid, geometry, bikes_data, weather_data, holiday_dates, strike_dates, start, end):
    data_for_modelling = gpd.sjoin(gpd.GeoDataFrame(bikes_data[['STARTLON', 'STARTLAT', 'STARTTIME']], geometry=geometry,crs='EPSG:4326'), grid, how='left', predicate='within')
    data_for_modelling = data_for_modelling[data_for_modelling['index_right'].isin([1418, 1417, 1416, 1439, 1438, 1437, 1500, 1499, 1498, 1541, 1540, 1539, 1582, 1581, 1580])]
    hourly_aggregated_starts = data_for_modelling.resample('1h', on='STARTTIME')['STARTTIME'].count()

    merged = weather_data.join(hourly_aggregated_starts, on='date', how='right')
    merged = merged[(merged['date'] >= start) & (merged['date'] <= end)]
    merged.rename(columns={'STARTTIME': 'trip_start_count'}, inplace=True)
    merged.fillna({'trip_start_count': 0}, inplace=True)
    merged['hour'] = merged['date'].dt.hour
    merged['day_of_week'] = merged['date'].dt.dayofweek
    merged['month'] = merged['date'].dt.month
    merged['is_weekend_or_holiday'] = (merged['day_of_week'].isin([5, 6]) | merged['date'].dt.date.isin(holiday_dates)).astype(int)
    merged['is_strike'] = merged['date'].dt.date.isin(strike_dates).astype(int)
    # merged = merged.drop(columns=['date'])
    return merged

holiday_dates_2023 = pd.to_datetime([
    "2023-01-01",
    "2023-01-06",
    "2023-04-07",
    "2023-04-10",
    "2023-05-01",
    "2023-05-18",
    "2023-05-29",
    "2023-06-08",
    "2023-08-15",
    "2023-10-03",
    "2023-11-01",
    "2023-12-25",
    "2023-12-26"
]).date

strikes_dates_2023 = pd.to_datetime([
    "2023-03-02",
    "2023-03-03",
    "2023-03-27",
    "2023-05-19"
]).date

data_for_modelling = prepare_data_for_modelling(grid, starts_geometry, data_filtered, weather_2023_df, holiday_dates_2023, strikes_dates_2023, '2023-01-01', '2024-01-01')
data_for_modelling

data_for_modelling.describe()

fig = plt.figure(figsize=(15, 8));
sns.lineplot(data_for_modelling[['date', 'trip_start_count', 'precipitation', 'temperature_2m']].query('date > "2023-08-26" & date < "2023-08-31"').set_index('date'));
plt.title('How Rain and Temperature Affected Trip Starts')

<Axes: xlabel='date'>

Text(0.5, 1.0, 'How Rain and Temperature Affected Trip Starts')

groups = data_for_modelling['date'].dt.date  
cv = GroupKFold(n_splits=5, shuffle=True, random_state=42)

# Define features and target
X = data_for_modelling.drop(['trip_start_count', 'date'], axis=1)
y = data_for_modelling['trip_start_count']

y

2        1
3       15
4       12
5       15
6       13
        ..
8757     2
8758     3
8759     5
8760     4
8761     9
Name: trip_start_count, Length: 8760, dtype: int64

X

%%time

xgb = XGBRegressor(objective='reg:squarederror', random_state=42)

param_grid = {
    'regressor__n_estimators': [100, 200],
    'regressor__max_depth': [3, 5, 7],
    'regressor__learning_rate': [0.01, 0.1],
    'regressor__subsample': [0.8, 1.0],
    'regressor__colsample_bytree': [0.8, 1.0],
    'regressor__reg_alpha': [0, 0.1],
    'regressor__reg_lambda': [1, 10]
}


# Wrap XGBoost with target transformation
model = TransformedTargetRegressor(
    regressor=xgb,
    func=np.log1p,     # transform y before fitting
    inverse_func=np.expm1  # inverse transform ŷ before scoring
)

grid_search = GridSearchCV(
    estimator=model,
    param_grid=param_grid,
    cv=cv,
    scoring='r2',
    return_train_score=True,
    verbose=2,
    n_jobs=-1
)

grid_search.fit(X, y, groups=groups)

print("Best parameters:", grid_search.best_params_)
print("Best CV R² score:", grid_search.best_score_)

Fitting 5 folds for each of 192 candidates, totalling 960 fits
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.5s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.5s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.5s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.5s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=0.8, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.5s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4sBest parameters: {'regressor__colsample_bytree': 1.0, 'regressor__learning_rate': 0.1, 'regressor__max_depth': 5, 'regressor__n_estimators': 200, 'regressor__reg_alpha': 0, 'regressor__reg_lambda': 10, 'regressor__subsample': 0.8}
Best CV R² score: 0.8304430365562439
CPU times: user 4.5 s, sys: 697 ms, total: 5.2 s
Wall time: 47.5 s

# choosing the best model and training it on the whole 2023 dataset
best_model = grid_search.best_estimator_
best_model.fit(X, y)

TransformedTargetRegressor(func=<ufunc 'log1p'>, inverse_func=<ufunc 'expm1'>,
                           regressor=XGBRegressor(base_score=None, booster=None,
                                                  callbacks=None,
                                                  colsample_bylevel=None,
                                                  colsample_bynode=None,
                                                  colsample_bytree=1.0,
                                                  device=None,
                                                  early_stopping_rounds=None,
                                                  enable_categorical=False,
                                                  eval_metric=None,
                                                  feature_types=None,
                                                  feature_weights=None,
                                                  gamma=None, grow_policy=None,
                                                  importance_type=None,
                                                  interaction_constraints=None,
                                                  learning_rate=0.1,
                                                  max_bin=None,
                                                  max_cat_threshold=None,
                                                  max_cat_to_onehot=None,
                                                  max_delta_step=None,
                                                  max_depth=5, max_leaves=None,
                                                  min_child_weight=None,
                                                  missing=nan,
                                                  monotone_constraints=None,
                                                  multi_strategy=None,
                                                  n_estimators=200, n_jobs=None,
                                                  num_parallel_tree=None, ...))

TransformedTargetRegressor(func=<ufunc 'log1p'>, inverse_func=<ufunc 'expm1'>,
                           regressor=XGBRegressor(base_score=None, booster=None,
                                                  callbacks=None,
                                                  colsample_bylevel=None,
                                                  colsample_bynode=None,
                                                  colsample_bytree=1.0,
                                                  device=None,
                                                  early_stopping_rounds=None,
                                                  enable_categorical=False,
                                                  eval_metric=None,
                                                  feature_types=None,
                                                  feature_weights=None,
                                                  gamma=None, grow_policy=None,
                                                  importance_type=None,
                                                  interaction_constraints=None,
                                                  learning_rate=0.1,
                                                  max_bin=None,
                                                  max_cat_threshold=None,
                                                  max_cat_to_onehot=None,
                                                  max_delta_step=None,
                                                  max_depth=5, max_leaves=None,
                                                  min_child_weight=None,
                                                  missing=nan,
                                                  monotone_constraints=None,
                                                  multi_strategy=None,
                                                  n_estimators=200, n_jobs=None,
                                                  num_parallel_tree=None, ...))

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=1.0, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.1, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=5,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=1.0, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.1, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=5,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)

data_2022 = pd.read_csv('MVG_Rad_Fahrten_2022.csv', sep=';', decimal=',')
data_2022.columns = data_2022.columns.str.strip()
data_2022.drop(columns=['Row'], inplace=True)
data_2022.reset_index(names='trip_index', inplace=True)

for col in ['STARTTIME', 'ENDTIME']:
    data_2022[col] = pd.to_datetime(data_2022[col])

for col in [ 'STARTLAT', 'STARTLON', 'ENDLAT', 'ENDLON',  'RENTAL_STATION_NAME', 'RETURN_IS_STATION', 'RETURN_STATION_NAME']:
    data_2022[col] = data_2022[col].str.strip()

for col in [ 'STARTLAT', 'STARTLON', 'ENDLAT', 'ENDLON', ]:
    data_2022[col] = data_2022[col].astype(float)


data_2022.loc[data_2022['RENTAL_STATION_NAME'] == "Schwanthalerhöhe", 'STARTLON'] = 11.54453
data_2022.loc[data_2022['RETURN_STATION_NAME'] == "Schwanthalerhöhe", 'ENDLON'] = 11.54453

data_2022.loc[data_2022['RENTAL_STATION_NAME'] == "Schwanthalerhöhe", 'STARTLAT'] = 48.13363
data_2022.loc[data_2022['RETURN_STATION_NAME'] == "Schwanthalerhöhe", 'ENDLAT'] = 48.13363

ranges = {'STARTLAT': (47.9, 48.32), 'ENDLAT': (47.9, 48.32), 'STARTLON': (11.2, 12), 'ENDLON': (11.2, 12)}

query_str = ' & '.join([f'{col} >= {low} & {col} <= {high}' for col, (low, high) in ranges.items()])
data_2022_filtered = data_2022.query(query_str).copy()
data_2022_filtered

/var/folders/0j/hc4b4c153dzfbvkzcxfbtjv80000gn/T/ipykernel_1324/4087005322.py:1: DtypeWarning: Columns (9) have mixed types. Specify dtype option on import or set low_memory=False.
  data_2022 = pd.read_csv('MVG_Rad_Fahrten_2022.csv', sep=';', decimal=',')

strikes_dates_2022 = []
holiday_dates_2022 = pd.to_datetime([
    "2022-01-01",
    "2022-01-06",
    "2022-04-15",
    "2022-04-18",
    "2022-05-01",
    "2022-05-26",
    "2022-06-06",
    "2022-06-16",
    "2022-08-15",
    "2022-10-03",
    "2022-11-01",
    "2022-12-25",
    "2022-12-26"
]).date

weather_2022_df = get_weather_data("2022-01-01", "2023-01-01", 48.1374, 11.5755)
weather_2022_df

Coordinates 48.1195068359375°N 11.550000190734863°E
Elevation 524.0 m asl
Timezone b'Europe/Berlin' b'GMT+2'
Timezone difference to GMT+0 7200 s

starts_geometry_2022 = [Point(xy) for xy in zip(data_2022_filtered['STARTLON'], data_2022_filtered['STARTLAT'])]

test_data = prepare_data_for_modelling(grid, starts_geometry_2022, data_2022_filtered, weather_2022_df, holiday_dates_2022, strikes_dates_2022, '2022-01-01', '2023-01-01')
test_data

X_test = test_data.drop(['trip_start_count', 'date'], axis=1)
y_test = test_data['trip_start_count']

test_score = best_model.score(X_test, y_test)
test_score

0.7966654300689697

y_pred = best_model.predict(X_test)

y_test_pred = pd.DataFrame({'y_test': y_test.to_numpy(), 'y_pred': y_pred, 'temperature': test_data['temperature_2m'].to_numpy(),
                           'precipitation': test_data['precipitation'].to_numpy()}, index=test_data['date'])
y_test_pred

fig = plt.figure(figsize=(15, 8));
sns.lineplot(y_test_pred[['y_test', 'y_pred']]);
plt.title('Predicted vs. Actual Trip Start Count in 2022 );

  Cell In[111], line 3
    plt.title('Predicted vs. Actual Trip Start Count in 2022 );
              ^
SyntaxError: unterminated string literal (detected at line 3)

prediction_showcase = y_test_pred.loc['2022-03-30': '2022-04-06', :]

fig, ax1 = plt.subplots(figsize=(15, 8));

# Plot regular series on left Y-axis
ax1.plot(prediction_showcase.index, prediction_showcase['y_test'], label='y_test', color='tab:blue');
ax1.plot(prediction_showcase.index, prediction_showcase['y_pred'], label='y_pred', color='tab:red');
ax1.plot(prediction_showcase.index, prediction_showcase['temperature'], label='temperature', color='tab:orange',  linestyle='--');
ax1.set_ylabel("Trip count, temperature");
ax1.tick_params(axis='y');
ax1.legend(loc='upper left');

# Create right Y-axis
ax2 = ax1.twinx();
ax2.plot(prediction_showcase.index, prediction_showcase['precipitation'], label='precipitation', color='tab:green', linestyle='--');
ax2.set_ylabel("Precipitation");
ax2.tick_params(axis='y', labelcolor='tab:green');
ax2.legend(loc='upper right');

plt.title("Predicted vs Actual Trip Counts with Key Features");
plt.tight_layout();

[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.7s

[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s

[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s

[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s

[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.7s

[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.7s

[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.6s

[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.8s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.01, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.9s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.1s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=3, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.2s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=5, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.3s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=100, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.4s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=1, regressor__subsample=1.0; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=1, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=0.8; total time=   0.7s
[CV] END regressor__colsample_bytree=1.0, regressor__learning_rate=0.1, regressor__max_depth=7, regressor__n_estimators=200, regressor__reg_alpha=0.1, regressor__reg_lambda=10, regressor__subsample=1.0; total time=   0.7s

	Row	STARTTIME	ENDTIME	STARTLAT	STARTLON	ENDLAT	ENDLON	RENTAL_IS_STATION	RENTAL_STATION_NAME	RETURN_IS_STATION	RETURN_STATION_NAME
0	1	2023-01-01 00:26	2023-01-01 00:51	48.13795	11.54569	48.16123	11.55782	0		1	Barbarastr
1	2	2023-01-01 00:30	2023-01-01 00:42	48.12903	11.54431	48.14797	11.53445	0		0
2	3	2023-01-01 00:32	2023-01-01 00:45	48.16841	11.55566	48.16467	11.57649	0		0
3	4	2023-01-01 00:34	2023-01-01 00:46	48.16843	11.55567	48.16464	11.57648	0		0
4	5	2023-01-01 00:35	2023-01-01 00:51	48.17104	11.54878	48.16243	11.53007	0		0
...	...	...	...	...	...	...	...	...	...	...	...
710101	710102	2023-12-31 23:52	2023-12-31 23:57	48.16719	11.55854	48.16917	11.55547	0		0
710102	710103	2023-12-31 23:52	2024-01-01 00:07	48.17061	11.57391	48.17334	11.55952	0		0
710103	710104	2023-12-31 23:53	2023-12-31 23:57	48.14131	11.56144	48.14094	11.56044	0		0
710104	710105	2023-12-31 23:54	2023-12-31 23:59	48.12353	11.54494	48.12674	11.54758	0		0
710105	710106	2023-12-31 23:54	2024-01-01 00:06	48.12376	11.54871	48.12376	11.54871	1	Kreisverwaltungsreferat	1	Kreisverwaltungsreferat

	trip_index	STARTTIME	ENDTIME	STARTLAT	STARTLON	ENDLAT	ENDLON	RENTAL_IS_STATION	RENTAL_STATION_NAME	RETURN_IS_STATION	RETURN_STATION_NAME
0	0	2023-01-01 00:26	2023-01-01 00:51	48.13795	11.54569	48.16123	11.55782	0		1	Barbarastr
1	1	2023-01-01 00:30	2023-01-01 00:42	48.12903	11.54431	48.14797	11.53445	0		0
2	2	2023-01-01 00:32	2023-01-01 00:45	48.16841	11.55566	48.16467	11.57649	0		0
3	3	2023-01-01 00:34	2023-01-01 00:46	48.16843	11.55567	48.16464	11.57648	0		0
4	4	2023-01-01 00:35	2023-01-01 00:51	48.17104	11.54878	48.16243	11.53007	0		0
...	...	...	...	...	...	...	...	...	...	...	...
710101	710101	2023-12-31 23:52	2023-12-31 23:57	48.16719	11.55854	48.16917	11.55547	0		0
710102	710102	2023-12-31 23:52	2024-01-01 00:07	48.17061	11.57391	48.17334	11.55952	0		0
710103	710103	2023-12-31 23:53	2023-12-31 23:57	48.14131	11.56144	48.14094	11.56044	0		0
710104	710104	2023-12-31 23:54	2023-12-31 23:59	48.12353	11.54494	48.12674	11.54758	0		0
710105	710105	2023-12-31 23:54	2024-01-01 00:06	48.12376	11.54871	48.12376	11.54871	1	Kreisverwaltungsreferat	1	Kreisverwaltungsreferat

	lat	lon
station
	5.698372	3.448503
AGROB Nord Ismaning	0.000000	0.000000
AGROB Süd Ismaning	0.000000	0.000000
Ackermannstraße	0.000000	0.000000
Ahornring Taufkirchen	0.000000	0.000000

	trip_index	STARTTIME	ENDTIME	STARTLAT	STARTLON	ENDLAT	ENDLON
count	710106.000000	710106	710106	710106.000000	710106.000000	710106.000000	710106.000000
mean	355052.500000	2023-07-03 20:01:02.147932928	2023-07-03 20:28:35.743255296	47.610000	11.772959	47.589505	11.720168
min	0.000000	2023-01-01 00:26:00	2023-01-01 00:42:00	0.000000	-71.178000	-55.973800	-99.259350
25%	177526.250000	2023-05-06 16:43:00	2023-05-06 17:17:15	48.128250	11.549040	48.128300	11.549040
50%	355052.500000	2023-07-04 19:35:00	2023-07-04 19:54:00	48.143190	11.567820	48.143190	11.567710
75%	532578.750000	2023-09-07 20:35:00	2023-09-07 21:04:00	48.159040	11.584500	48.159020	11.584430
max	710105.000000	2023-12-31 23:54:00	2024-01-01 16:00:00	53.094660	141.353220	53.094660	141.353220
std	204990.089464	NaN	NaN	5.045196	3.141217	5.170640	3.034583

	trip_index	STARTTIME	ENDTIME	STARTLAT	STARTLON	ENDLAT	ENDLON	RENTAL_STATION_NAME	RETURN_STATION_NAME
0	0	2023-01-01 00:26:00	2023-01-01 00:51:00	48.13795	11.54569	48.16123	11.55782		Barbarastr
1	1	2023-01-01 00:30:00	2023-01-01 00:42:00	48.12903	11.54431	48.14797	11.53445
2	2	2023-01-01 00:32:00	2023-01-01 00:45:00	48.16841	11.55566	48.16467	11.57649
3	3	2023-01-01 00:34:00	2023-01-01 00:46:00	48.16843	11.55567	48.16464	11.57648
4	4	2023-01-01 00:35:00	2023-01-01 00:51:00	48.17104	11.54878	48.16243	11.53007
...	...	...	...	...	...	...	...	...	...
710101	710101	2023-12-31 23:52:00	2023-12-31 23:57:00	48.16719	11.55854	48.16917	11.55547
710102	710102	2023-12-31 23:52:00	2024-01-01 00:07:00	48.17061	11.57391	48.17334	11.55952
710103	710103	2023-12-31 23:53:00	2023-12-31 23:57:00	48.14131	11.56144	48.14094	11.56044
710104	710104	2023-12-31 23:54:00	2023-12-31 23:59:00	48.12353	11.54494	48.12674	11.54758
710105	710105	2023-12-31 23:54:00	2024-01-01 00:06:00	48.12376	11.54871	48.12376	11.54871	Kreisverwaltungsreferat	Kreisverwaltungsreferat

Executive Summary¶

Data Loading and Cleaning¶

Visualization: Trip Start and End Locations¶

Trip Start Locations (marker size reflects the number of trip starts)

Trip End Locations (marker size reflects the number of trip ends)

Visualization: Trip Start and End Density Distribution¶

Trip Start Density (lighter colour reflects higher density)

Trip End Density (lighter colour reflects higher density)

Visualization: Major Trip Flows (1000+ trips)¶

Major Trip Flows (marker and line thickness reflects the number of trips)

Trip Duration Distribution¶

Trip Distance Distribution¶

Trip Starts Time series analysis¶

Stations¶

Visualization: Stations as Trip Start and End Locations¶

Trip Starts at Stations (marker size reflects the number of trip starts)

Trip Ends at Stations (marker size reflects the number of trip starts)

Visualization: Trip start and end time series per station¶

Trip history reconstruction¶

Trip chain 255855

Trip chain 629025

Trip chain 685353

Idle duration¶

Locations With Exceptionally Long Bike Idle Times

Trip count prediction¶

	STARTLAT	STARTLON	ENDLAT	ENDLON
min	47.91573	11.20252	47.91573	11.20252
max	48.31310	11.95886	48.31479	11.95886

	STATION_NAME	latitude	longitude	n_trips
0	AGROB Nord Ismaning	48.21102	11.66092	73
1	AGROB Süd Ismaning	48.20836	11.65885	151
2	Ackermannstraße	48.16824	11.56469	828
3	Ahornring Taufkirchen	48.04797	11.59898	74
4	Ainmillerstraße	48.15904	11.57756	707
...	...	...	...	...
326	Würmtalstraße Gräfelfing	48.11545	11.44003	163
327	ZHS Olympiazentrum	48.18079	11.54656	663
328	Zenettiplatz	48.12452	11.55557	1176
329	Zugspitzstraße Neuried	48.08800	11.46974	110
330	astopark	48.09174	11.28297	339

	STARTLON	STARTLAT	geometry_s	index_right_s	ENDLON	ENDLAT	geometry_f	index_right_f
0	11.54569	48.13795	POINT (11.54569 48.13795)	1416.0	11.55782	48.16123	POINT (11.55782 48.16123)	1460.0
1	11.54431	48.12903	POINT (11.54431 48.12903)	1415.0	11.53445	48.14797	POINT (11.53445 48.14797)	1376.0
2	11.55566	48.16841	POINT (11.55566 48.16841)	1460.0	11.57649	48.16467	POINT (11.57649 48.16467)	1542.0
3	11.55567	48.16843	POINT (11.55567 48.16843)	1460.0	11.57648	48.16464	POINT (11.57648 48.16464)	1542.0
4	11.54878	48.17104	POINT (11.54878 48.17104)	1420.0	11.53007	48.16243	POINT (11.53007 48.16243)	1378.0
...	...	...	...	...	...	...	...	...
694652	11.55854	48.16719	POINT (11.55854 48.16719)	1460.0	11.55547	48.16917	POINT (11.55547 48.16917)	1460.0
694653	11.57391	48.17061	POINT (11.57391 48.17061)	1543.0	11.55952	48.17334	POINT (11.55952 48.17334)	1461.0
694654	11.56144	48.14131	POINT (11.56144 48.14131)	1499.0	11.56044	48.14094	POINT (11.56044 48.14094)	1499.0
694655	11.54494	48.12353	POINT (11.54494 48.12353)	1415.0	11.54758	48.12674	POINT (11.54758 48.12674)	1415.0
694656	11.54871	48.12376	POINT (11.54871 48.12376)	1415.0	11.54871	48.12376	POINT (11.54871 48.12376)	1415.0

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	trip_duration_minutes
count	694657.000000
mean	26.332067
std	133.764000
min	-56.000000
25%	7.000000
50%	11.000000
75%	19.000000
max	14557.000000

	trip_index	STARTTIME	ENDTIME	STARTLAT	STARTLON	ENDLAT	ENDLON	RENTAL_STATION_NAME	RETURN_STATION_NAME	trip_duration_minutes
629483	643458	2023-10-29 02:37:00	2023-10-29 02:16:00	48.12044	11.57651	48.17835	11.57484	Kolumbusplatz	NaN	-21.0
629490	643465	2023-10-29 02:41:00	2023-10-29 02:00:00	48.13998	11.50744	48.11326	11.53177	NaN	NaN	-41.0
629503	643478	2023-10-29 02:49:00	2023-10-29 02:04:00	48.14395	11.55688	48.17062	11.53471	NaN	NaN	-45.0
629504	643479	2023-10-29 02:50:00	2023-10-29 02:18:00	48.12452	11.55557	48.13293	11.50309	Zenettiplatz	NaN	-32.0
629507	643482	2023-10-29 02:52:00	2023-10-29 02:10:00	48.13000	11.57317	48.13107	11.60504	NaN	NaN	-42.0
629508	643483	2023-10-29 02:52:00	2023-10-29 02:07:00	48.16184	11.58603	48.15727	11.56178	NaN	NaN	-45.0
629510	643485	2023-10-29 02:54:00	2023-10-29 02:32:00	48.18261	11.55426	48.16695	11.62009	NaN	NaN	-22.0
629514	643489	2023-10-29 02:55:00	2023-10-29 02:25:00	48.15124	11.56574	48.13593	11.63376	NaN	Berg am Laim	-30.0
629515	643490	2023-10-29 02:55:00	2023-10-29 02:31:00	48.18258	11.55436	48.16693	11.62008	NaN	NaN	-24.0
629519	643494	2023-10-29 02:56:00	2023-10-29 02:06:00	48.13114	11.58220	48.13114	11.58220	Boschbrücke	Boschbrücke	-50.0
629520	643495	2023-10-29 02:57:00	2023-10-29 02:01:00	48.14385	11.58622	48.14355	11.58616	NaN	NaN	-56.0
629521	643496	2023-10-29 02:57:00	2023-10-29 02:06:00	48.15427	11.57630	48.14883	11.57755	NaN	NaN	-51.0
629522	643497	2023-10-29 02:58:00	2023-10-29 02:11:00	48.12452	11.55557	48.11521	11.57917	Zenettiplatz	NaN	-47.0
629524	643499	2023-10-29 02:59:00	2023-10-29 02:27:00	48.17161	11.50712	48.19163	11.55271	NaN	NaN	-32.0

	trip_distance_km	trip_duration_minutes
trip_distance_km	1.000000	0.021396
trip_duration_minutes	0.021396	1.000000

		STARTTIME
STARTLAT	STARTLON
48.15128	11.55832	3476
48.14818	11.56833	2623
48.14210	11.56099	2601
48.16199	11.58654	2402
48.14954	11.58065	2377
48.17842	11.55658	2370
48.14281	11.54874	2157
48.14984	11.58450	1971
48.14873	11.46176	1797
48.15291	11.53310	1753

	trip_index	timestamp	location	kind
0	0	2023-01-01 00:26:00	(48.13795, 11.54569)	start
1	1	2023-01-01 00:30:00	(48.12903, 11.54431)	start
2	2	2023-01-01 00:32:00	(48.16841, 11.55566)	start
3	3	2023-01-01 00:34:00	(48.16843, 11.55567)	start
4	4	2023-01-01 00:35:00	(48.17104, 11.54878)	start
...	...	...	...	...
1389310	710102	2024-01-01 00:07:00	(48.17334, 11.55952)	end
1389304	710096	2024-01-01 00:08:00	(48.17337, 11.55951)	end
1388734	709522	2024-01-01 00:09:00	(48.18169, 11.57342)	end
1389308	710100	2024-01-01 00:39:00	(48.24959, 11.65319)	end
1389124	709916	2024-01-01 16:00:00	(48.12701, 11.55865)	end

	trip_index	STARTTIME	ENDTIME	STARTLAT	STARTLON	ENDLAT	ENDLON	RENTAL_STATION_NAME	RETURN_STATION_NAME	trip_duration_minutes	trip_distance_km	start_coord_zipped	end_coord_zipped	assumed_bike_number_at_starttime	assumed_bike_number_at_endtime	NEXTTRIP	PREVIOUSTRIP	trip_chain_index
259600	255855	2023-06-04 19:42:00	2023-06-04 19:51:00	48.14666	11.57032	48.15718	11.57123	NaN	NaN	9.0	1.171711	(48.14666, 11.57032)	(48.15718, 11.57123)	1	0	256084.0	NaN	255855
259601	256084	2023-06-04 21:15:00	2023-06-04 21:43:00	48.15718	11.57123	48.12156	11.57343	NaN	NaN	28.0	3.964079	(48.15718, 11.57123)	(48.12156, 11.57343)	1	0	257955.0	255855.0	255855
259602	257955	2023-06-05 18:24:00	2023-06-05 18:51:00	48.12156	11.57343	48.13695	11.58059	NaN	NaN	27.0	1.792337	(48.12156, 11.57343)	(48.13695, 11.58059)	1	0	258617.0	256084.0	255855
259603	258617	2023-06-05 22:42:00	2023-06-05 22:53:00	48.13695	11.58059	48.11798	11.57412	NaN	NaN	11.0	2.163616	(48.13695, 11.58059)	(48.11798, 11.57412)	1	0	259079.0	257955.0	255855
259604	259079	2023-06-06 08:53:00	2023-06-06 09:05:00	48.11798	11.57412	48.12043	11.60262	NaN	NaN	12.0	2.139334	(48.11798, 11.57412)	(48.12043, 11.60262)	1	0	260286.0	258617.0	255855
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
259660	340813	2023-06-29 20:41:00	2023-06-29 20:54:00	48.15744	11.59416	48.16072	11.56746	NaN	NaN	13.0	2.019569	(48.15744, 11.59416)	(48.16072, 11.56746)	1	0	341054.0	340254.0	255855
259661	341054	2023-06-29 21:41:00	2023-06-29 21:51:00	48.16072	11.56746	48.16756	11.55479	NaN	NaN	10.0	1.211100	(48.16072, 11.56746)	(48.16756, 11.55479)	1	0	341240.0	340813.0	255855
259662	341240	2023-06-29 22:23:00	2023-06-29 22:45:00	48.16756	11.55479	48.12598	11.54939	NaN	NaN	22.0	4.640842	(48.16756, 11.55479)	(48.12598, 11.54939)	1	0	344563.0	341054.0	255855
259663	344563	2023-07-01 13:39:00	2023-07-01 13:46:00	48.12598	11.54939	48.13285	11.56585	NaN	NaN	7.0	1.443881	(48.12598, 11.54939)	(48.13285, 11.56585)	1	0	344901.0	341240.0	255855
259664	344901	2023-07-01 15:03:00	2023-07-01 15:12:00	48.13285	11.56585	48.11968	11.54820	NaN	Implerstraße	9.0	1.967455	(48.13285, 11.56585)	(48.11968, 11.5482)	1	-257	NaN	344563.0	255855

	trip_index_current	STARTTIME_current	ENDTIME_current	STARTLAT_current	STARTLON_current	ENDLAT_current	ENDLON_current	RENTAL_STATION_NAME_current	RETURN_STATION_NAME_current	trip_duration_minutes_current	trip_distance_km_current	start_coord_zipped_current	end_coord_zipped_current	assumed_bike_number_at_starttime_current	assumed_bike_number_at_endtime_current	trip_index_next	STARTTIME_next	ENDTIME_next	STARTLAT_next	STARTLON_next	ENDLAT_next	ENDLON_next	RENTAL_STATION_NAME_next	RETURN_STATION_NAME_next	trip_duration_minutes_next	trip_distance_km_next	start_coord_zipped_next	end_coord_zipped_next	assumed_bike_number_at_starttime_next	assumed_bike_number_at_endtime_next
0	0	2023-01-01 00:26:00	2023-01-01 00:51:00	48.13795	11.54569	48.16123	11.55782	NaN	Barbarastr	25.0	2.741423	(48.13795, 11.54569)	(48.16123, 11.55782)	0	0	32	2023-01-01 01:06:00	2023-01-01 01:20:00	48.16123	11.55782	48.15818	11.57227	Barbarastr	NaN	14.0	1.127231	(48.16123, 11.55782)	(48.15818, 11.57227)	1	0
1	0	2023-01-01 00:26:00	2023-01-01 00:51:00	48.13795	11.54569	48.16123	11.55782	NaN	Barbarastr	25.0	2.741423	(48.13795, 11.54569)	(48.16123, 11.55782)	0	0	391	2023-01-01 11:16:00	2023-01-01 11:37:00	48.16123	11.55782	48.13517	11.54300	Barbarastr	NaN	21.0	3.100444	(48.16123, 11.55782)	(48.13517, 11.543)	0	0
2	0	2023-01-01 00:26:00	2023-01-01 00:51:00	48.13795	11.54569	48.16123	11.55782	NaN	Barbarastr	25.0	2.741423	(48.13795, 11.54569)	(48.16123, 11.55782)	0	0	563	2023-01-01 13:44:00	2023-01-01 14:04:00	48.16123	11.55782	48.18738	11.51868	Barbarastr	NaN	20.0	4.114442	(48.16123, 11.55782)	(48.18738, 11.51868)	-1	0
3	0	2023-01-01 00:26:00	2023-01-01 00:51:00	48.13795	11.54569	48.16123	11.55782	NaN	Barbarastr	25.0	2.741423	(48.13795, 11.54569)	(48.16123, 11.55782)	0	0	1287	2023-01-02 08:54:00	2023-01-02 09:04:00	48.16123	11.55782	48.16199	11.58654	Barbarastr	Münchner Freiheit	10.0	2.138208	(48.16123, 11.55782)	(48.16199, 11.58654)	-2	0
4	0	2023-01-01 00:26:00	2023-01-01 00:51:00	48.13795	11.54569	48.16123	11.55782	NaN	Barbarastr	25.0	2.741423	(48.13795, 11.54569)	(48.16123, 11.55782)	0	0	3293	2023-01-04 08:35:00	2023-01-04 08:48:00	48.16123	11.55782	48.15664	11.58934	Barbarastr	Thiemestraße	13.0	2.399856	(48.16123, 11.55782)	(48.15664, 11.58934)	-1	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
132104396	710047	2023-12-31 21:09:00	2023-12-31 21:15:00	48.24959	11.65319	48.25210	11.64625	Maibaumplatz Garching	NaN	6.0	0.586104	(48.24959, 11.65319)	(48.2521, 11.64625)	-50	0	710075	2023-12-31 23:01:00	2023-12-31 23:04:00	48.25210	11.64625	48.25206	11.64628	NaN	NaN	3.0	0.004975	(48.2521, 11.64625)	(48.25206, 11.64628)	1	0
132104397	710048	2023-12-31 21:09:00	2023-12-31 21:17:00	48.13264	11.58896	48.12867	11.56932	NaN	NaN	8.0	1.527130	(48.13264, 11.58896)	(48.12867, 11.56932)	1	0	710053	2023-12-31 21:32:00	2023-12-31 21:46:00	48.12867	11.56932	48.11155	11.56616	NaN	NaN	14.0	1.918102	(48.12867, 11.56932)	(48.11155, 11.56616)	1	0
132106694	710057	2023-12-31 21:55:00	2023-12-31 22:05:00	48.13657	11.58007	48.13337	11.57633	NaN	NaN	10.0	0.451770	(48.13657, 11.58007)	(48.13337, 11.57633)	1	0	710061	2023-12-31 22:08:00	2023-12-31 22:28:00	48.13337	11.57633	48.13036	11.55299	NaN	NaN	20.0	1.769258	(48.13337, 11.57633)	(48.13036, 11.55299)	1	0
132106696	710061	2023-12-31 22:08:00	2023-12-31 22:28:00	48.13337	11.57633	48.13036	11.55299	NaN	NaN	20.0	1.769258	(48.13337, 11.57633)	(48.13036, 11.55299)	1	0	710073	2023-12-31 23:01:00	2023-12-31 23:15:00	48.13036	11.55299	48.12626	11.53909	NaN	NaN	14.0	1.130697	(48.13036, 11.55299)	(48.12626, 11.53909)	1	0
132106697	710062	2023-12-31 22:11:00	2023-12-31 22:32:00	48.13203	11.57647	48.13035	11.55298	Gärtnerplatz	NaN	21.0	1.758451	(48.13203, 11.57647)	(48.13035, 11.55298)	-319	0	710074	2023-12-31 23:01:00	2023-12-31 23:15:00	48.13035	11.55298	48.12624	11.53905	NaN	NaN	14.0	1.133190	(48.13035, 11.55298)	(48.12624, 11.53905)	1	0

	trip_index_current	STARTTIME_current	ENDTIME_current	STARTLAT_current	STARTLON_current	ENDLAT_current	ENDLON_current	RENTAL_STATION_NAME_current	RETURN_STATION_NAME_current	trip_duration_minutes_current	trip_distance_km_current	start_coord_zipped_current	end_coord_zipped_current	assumed_bike_number_at_starttime_current	assumed_bike_number_at_endtime_current	trip_index_next	STARTTIME_next	ENDTIME_next	STARTLAT_next	STARTLON_next	ENDLAT_next	ENDLON_next	RENTAL_STATION_NAME_next	RETURN_STATION_NAME_next	trip_duration_minutes_next	trip_distance_km_next	start_coord_zipped_next	end_coord_zipped_next	assumed_bike_number_at_starttime_next	assumed_bike_number_at_endtime_next
0	0	2023-01-01 00:26:00	2023-01-01 00:51:00	48.13795	11.54569	48.16123	11.55782	NaN	Barbarastr	25.0	2.741423	(48.13795, 11.54569)	(48.16123, 11.55782)	0	0	32	2023-01-01 01:06:00	2023-01-01 01:20:00	48.16123	11.55782	48.15818	11.57227	Barbarastr	NaN	14.0	1.127231	(48.16123, 11.55782)	(48.15818, 11.57227)	1	0
705	1	2023-01-01 00:30:00	2023-01-01 00:42:00	48.12903	11.54431	48.14797	11.53445	NaN	NaN	12.0	2.230186	(48.12903, 11.54431)	(48.14797, 11.53445)	0	0	3464	2023-01-04 11:57:00	2023-01-04 12:06:00	48.14797	11.53445	48.14401	11.55768	NaN	NaN	9.0	1.783849	(48.14797, 11.53445)	(48.14401, 11.55768)	1	0
706	2	2023-01-01 00:32:00	2023-01-01 00:45:00	48.16841	11.55566	48.16467	11.57649	NaN	NaN	13.0	1.604274	(48.16841, 11.55566)	(48.16467, 11.57649)	0	0	1176	2023-01-02 02:02:00	2023-01-02 02:28:00	48.16467	11.57649	48.18308	11.61287	NaN	NaN	26.0	3.392864	(48.16467, 11.57649)	(48.18308, 11.61287)	1	0
707	3	2023-01-01 00:34:00	2023-01-01 00:46:00	48.16843	11.55567	48.16464	11.57648	NaN	NaN	12.0	1.604289	(48.16843, 11.55567)	(48.16464, 11.57648)	0	0	5227	2023-01-06 07:53:00	2023-01-06 08:06:00	48.16464	11.57648	48.17779	11.59179	NaN	NaN	13.0	1.853300	(48.16464, 11.57648)	(48.17779, 11.59179)	1	0
708	4	2023-01-01 00:35:00	2023-01-01 00:51:00	48.17104	11.54878	48.16243	11.53007	NaN	NaN	16.0	1.689230	(48.17104, 11.54878)	(48.16243, 11.53007)	0	0	11707	2023-01-11 14:59:00	2023-01-11 15:15:00	48.16243	11.53007	48.13932	11.52527	NaN	NaN	16.0	2.594378	(48.16243, 11.53007)	(48.13932, 11.52527)	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
132104396	710047	2023-12-31 21:09:00	2023-12-31 21:15:00	48.24959	11.65319	48.25210	11.64625	Maibaumplatz Garching	NaN	6.0	0.586104	(48.24959, 11.65319)	(48.2521, 11.64625)	-50	0	710075	2023-12-31 23:01:00	2023-12-31 23:04:00	48.25210	11.64625	48.25206	11.64628	NaN	NaN	3.0	0.004975	(48.2521, 11.64625)	(48.25206, 11.64628)	1	0
132104397	710048	2023-12-31 21:09:00	2023-12-31 21:17:00	48.13264	11.58896	48.12867	11.56932	NaN	NaN	8.0	1.527130	(48.13264, 11.58896)	(48.12867, 11.56932)	1	0	710053	2023-12-31 21:32:00	2023-12-31 21:46:00	48.12867	11.56932	48.11155	11.56616	NaN	NaN	14.0	1.918102	(48.12867, 11.56932)	(48.11155, 11.56616)	1	0
132106694	710057	2023-12-31 21:55:00	2023-12-31 22:05:00	48.13657	11.58007	48.13337	11.57633	NaN	NaN	10.0	0.451770	(48.13657, 11.58007)	(48.13337, 11.57633)	1	0	710061	2023-12-31 22:08:00	2023-12-31 22:28:00	48.13337	11.57633	48.13036	11.55299	NaN	NaN	20.0	1.769258	(48.13337, 11.57633)	(48.13036, 11.55299)	1	0
132106696	710061	2023-12-31 22:08:00	2023-12-31 22:28:00	48.13337	11.57633	48.13036	11.55299	NaN	NaN	20.0	1.769258	(48.13337, 11.57633)	(48.13036, 11.55299)	1	0	710073	2023-12-31 23:01:00	2023-12-31 23:15:00	48.13036	11.55299	48.12626	11.53909	NaN	NaN	14.0	1.130697	(48.13036, 11.55299)	(48.12626, 11.53909)	1	0
132106697	710062	2023-12-31 22:11:00	2023-12-31 22:32:00	48.13203	11.57647	48.13035	11.55298	Gärtnerplatz	NaN	21.0	1.758451	(48.13203, 11.57647)	(48.13035, 11.55298)	-319	0	710074	2023-12-31 23:01:00	2023-12-31 23:15:00	48.13035	11.55298	48.12624	11.53905	NaN	NaN	14.0	1.133190	(48.13035, 11.55298)	(48.12624, 11.53905)	1	0

	trip_index	STARTTIME	ENDTIME	STARTLAT	STARTLON	ENDLAT	ENDLON	RENTAL_STATION_NAME	RETURN_STATION_NAME	trip_duration_minutes	trip_distance_km	start_coord_zipped	end_coord_zipped	assumed_bike_number_at_starttime	assumed_bike_number_at_endtime	NEXTTRIP	PREVIOUSTRIP	trip_chain_index
622467	629025	2023-10-21 10:49:00	2023-10-21 10:52:00	48.17869	11.59786	48.17792	11.60184	NaN	NaN	3.0	0.308119	(48.17869, 11.59786)	(48.17792, 11.60184)	0	0	629335.0	NaN	629025
622468	629335	2023-10-21 12:46:00	2023-10-21 12:50:00	48.17792	11.60184	48.17869	11.59785	NaN	NaN	4.0	0.308833	(48.17792, 11.60184)	(48.17869, 11.59785)	1	0	629526.0	629025.0	629025
622469	629526	2023-10-21 13:53:00	2023-10-21 14:44:00	48.17869	11.59785	48.10723	11.55143	NaN	NaN	51.0	8.664319	(48.17869, 11.59785)	(48.10723, 11.55143)	1	0	629815.0	629335.0	629025
622470	629815	2023-10-21 15:26:00	2023-10-21 15:32:00	48.10723	11.55143	48.11199	11.55411	NaN	NaN	6.0	0.565652	(48.10723, 11.55143)	(48.11199, 11.55411)	1	0	629885.0	629526.0	629025
622471	629885	2023-10-21 15:47:00	2023-10-21 15:59:00	48.11199	11.55411	48.12283	11.54722	NaN	NaN	12.0	1.309956	(48.11199, 11.55411)	(48.12283, 11.54722)	1	0	630034.0	629815.0	629025
622472	630034	2023-10-21 16:36:00	2023-10-21 16:53:00	48.12283	11.54722	48.13632	11.57500	NaN	NaN	17.0	2.554636	(48.12283, 11.54722)	(48.13632, 11.575)	1	0	630116.0	629885.0	629025
622473	630116	2023-10-21 17:06:00	2023-10-21 17:26:00	48.13632	11.57500	48.11762	11.54011	NaN	NaN	20.0	3.327069	(48.13632, 11.575)	(48.11762, 11.54011)	1	0	630212.0	630034.0	629025
622474	630212	2023-10-21 17:44:00	2023-10-21 17:51:00	48.11762	11.54011	48.11257	11.53183	NaN	NaN	7.0	0.833911	(48.11762, 11.54011)	(48.11257, 11.53183)	1	0	631085.0	630116.0	629025
622475	631085	2023-10-22 11:46:00	2023-10-22 12:05:00	48.11257	11.53183	48.09461	11.56161	NaN	NaN	19.0	2.984477	(48.11257, 11.53183)	(48.09461, 11.56161)	1	0	650056.0	630212.0	629025
622476	650056	2023-11-02 05:20:00	2023-11-02 05:44:00	48.09461	11.56161	48.10874	11.57478	NaN	Wettersteinplatz	24.0	1.852197	(48.09461, 11.56161)	(48.10874, 11.57478)	1	-179	NaN	631085.0	629025

	trip_index	STARTTIME	ENDTIME	STARTLAT	STARTLON	ENDLAT	ENDLON	RENTAL_STATION_NAME	RETURN_STATION_NAME	trip_duration_minutes	trip_distance_km	start_coord_zipped	end_coord_zipped	assumed_bike_number_at_starttime	assumed_bike_number_at_endtime	NEXTTRIP	PREVIOUSTRIP	trip_chain_index
675438	685353	2023-11-28 21:11:00	2023-11-28 21:15:00	48.17869	11.59786	48.17787	11.60182	NaN	NaN	4.0	0.308289	(48.17869, 11.59786)	(48.17787, 11.60182)	0	0	685487.0	NaN	685353
675439	685487	2023-11-29 07:37:00	2023-11-29 07:42:00	48.17787	11.60182	48.17870	11.59789	NaN	NaN	5.0	0.306491	(48.17787, 11.60182)	(48.1787, 11.59789)	1	0	686190.0	685353.0	685353
675440	686190	2023-11-29 17:47:00	2023-11-29 17:55:00	48.17870	11.59789	48.18252	11.59343	NaN	NaN	8.0	0.538910	(48.1787, 11.59789)	(48.18252, 11.59343)	1	0	686259.0	685487.0	685353
675441	686259	2023-11-29 18:12:00	2023-11-29 18:15:00	48.18252	11.59343	48.18231	11.59214	NaN	NaN	3.0	0.098728	(48.18252, 11.59343)	(48.18231, 11.59214)	1	0	686444.0	686190.0	685353
675442	686444	2023-11-29 19:34:00	2023-11-29 19:39:00	48.18231	11.59214	48.18208	11.59382	NaN	NaN	5.0	0.127519	(48.18231, 11.59214)	(48.18208, 11.59382)	1	0	686466.0	686259.0	685353
675443	686466	2023-11-29 19:51:00	2023-11-29 19:59:00	48.18208	11.59382	48.17870	11.59785	NaN	NaN	8.0	0.480694	(48.18208, 11.59382)	(48.1787, 11.59785)	1	0	686548.0	686444.0	685353
675444	686548	2023-11-29 21:10:00	2023-11-29 21:14:00	48.17870	11.59785	48.17787	11.60182	NaN	NaN	4.0	0.309329	(48.1787, 11.59785)	(48.17787, 11.60182)	1	0	686746.0	686466.0	685353
675445	686746	2023-11-30 07:10:00	2023-11-30 07:14:00	48.17787	11.60182	48.17871	11.59789	NaN	NaN	4.0	0.306828	(48.17787, 11.60182)	(48.17871, 11.59789)	1	0	687226.0	686548.0	685353
675446	687226	2023-11-30 17:28:00	2023-11-30 17:37:00	48.17871	11.59789	48.18220	11.59216	NaN	NaN	9.0	0.576338	(48.17871, 11.59789)	(48.1822, 11.59216)	1	0	687350.0	686746.0	685353
675447	687350	2023-11-30 19:40:00	2023-11-30 19:52:00	48.18220	11.59216	48.17875	11.59784	NaN	NaN	12.0	0.570594	(48.1822, 11.59216)	(48.17875, 11.59784)	1	0	687380.0	687226.0	685353
675448	687380	2023-11-30 21:11:00	2023-11-30 21:15:00	48.17875	11.59784	48.17789	11.60183	NaN	NaN	4.0	0.311756	(48.17875, 11.59784)	(48.17789, 11.60183)	1	0	687509.0	687350.0	685353
675449	687509	2023-12-01 07:31:00	2023-12-01 07:35:00	48.17789	11.60183	48.17868	11.59787	NaN	NaN	4.0	0.307319	(48.17789, 11.60183)	(48.17868, 11.59787)	1	0	688631.0	687380.0	685353
675450	688631	2023-12-05 17:42:00	2023-12-05 17:54:00	48.17868	11.59787	48.18256	11.59351	NaN	NaN	12.0	0.539683	(48.17868, 11.59787)	(48.18256, 11.59351)	1	0	688641.0	687509.0	685353
675451	688641	2023-12-05 18:00:00	2023-12-05 18:03:00	48.18256	11.59351	48.18228	11.59209	NaN	NaN	3.0	0.110088	(48.18256, 11.59351)	(48.18228, 11.59209)	1	0	688721.0	688631.0	685353
675452	688721	2023-12-05 19:44:00	2023-12-05 19:57:00	48.18228	11.59209	48.17870	11.59783	NaN	NaN	13.0	0.583666	(48.18228, 11.59209)	(48.1787, 11.59783)	1	0	689117.0	688641.0	685353
675453	689117	2023-12-06 16:04:00	2023-12-06 16:14:00	48.17870	11.59783	48.18221	11.59210	NaN	NaN	10.0	0.577838	(48.1787, 11.59783)	(48.18221, 11.5921)	1	0	689172.0	688721.0	685353
675454	689172	2023-12-06 17:38:00	2023-12-06 17:42:00	48.18221	11.59210	48.18258	11.59358	NaN	NaN	4.0	0.117494	(48.18221, 11.5921)	(48.18258, 11.59358)	1	0	689194.0	689117.0	685353
675455	689194	2023-12-06 17:57:00	2023-12-06 18:06:00	48.18258	11.59358	48.17871	11.59791	NaN	NaN	9.0	0.537455	(48.18258, 11.59358)	(48.17871, 11.59791)	1	0	689846.0	689172.0	685353
675456	689846	2023-12-07 17:29:00	2023-12-07 17:40:00	48.17871	11.59791	48.18223	11.59210	NaN	NaN	11.0	0.582984	(48.17871, 11.59791)	(48.18223, 11.5921)	1	0	689986.0	689194.0	685353
675457	689986	2023-12-07 19:48:00	2023-12-07 19:58:00	48.18223	11.59210	48.17869	11.59782	NaN	NaN	10.0	0.579550	(48.18223, 11.5921)	(48.17869, 11.59782)	1	0	690554.0	689846.0	685353
675458	690554	2023-12-08 17:27:00	2023-12-08 17:39:00	48.17869	11.59782	48.18246	11.59232	NaN	NaN	12.0	0.585674	(48.17869, 11.59782)	(48.18246, 11.59232)	1	0	690625.0	689986.0	685353
675459	690625	2023-12-08 19:31:00	2023-12-08 19:41:00	48.18246	11.59232	48.17869	11.59784	NaN	NaN	10.0	0.586714	(48.18246, 11.59232)	(48.17869, 11.59784)	1	0	692621.0	690554.0	685353
675460	692621	2023-12-11 17:08:00	2023-12-11 17:26:00	48.17869	11.59784	48.18233	11.59213	NaN	NaN	18.0	0.586620	(48.17869, 11.59784)	(48.18233, 11.59213)	1	0	692840.0	690625.0	685353
675461	692840	2023-12-11 18:51:00	2023-12-11 18:59:00	48.18233	11.59213	48.17877	11.59777	NaN	NaN	8.0	0.576721	(48.18233, 11.59213)	(48.17877, 11.59777)	1	0	692990.0	692621.0	685353
675462	692990	2023-12-11 21:06:00	2023-12-11 21:09:00	48.17877	11.59777	48.17787	11.60182	NaN	NaN	3.0	0.317380	(48.17877, 11.59777)	(48.17787, 11.60182)	1	0	693143.0	692840.0	685353
675463	693143	2023-12-12 07:15:00	2023-12-12 07:19:00	48.17787	11.60182	48.17875	11.59782	NaN	NaN	4.0	0.313152	(48.17787, 11.60182)	(48.17875, 11.59782)	1	0	693779.0	692990.0	685353
675464	693779	2023-12-12 17:45:00	2023-12-12 17:53:00	48.17875	11.59782	48.18230	11.59210	NaN	NaN	8.0	0.580305	(48.17875, 11.59782)	(48.1823, 11.5921)	1	1	NaN	693143.0	685353

	idle_time_to_next_trip_days
count	522951.000000
mean	0.891344
std	2.632067
min	0.000000
25%	0.052778
50%	0.227083
75%	0.786111
max	271.376389

	trip_index	timestamp	location	kind	n_starts_before	n_ends_before	assumed_bike_number_at_timestamp
58271	29908	2023-01-29 13:11:00	(48.1172, 11.60153)	start	0	1	1
58282	29908	2023-01-29 13:18:00	(48.12469, 11.59479)	end	0	0	0

	date	temperature_2m	precipitation	wind_speed_10m
0	2022-12-31 22:00:00	7.4215	0.0	12.722830
1	2022-12-31 23:00:00	7.6215	0.0	12.245293
2	2023-01-01 00:00:00	7.5715	0.0	11.726277
3	2023-01-01 01:00:00	7.6215	0.0	13.237039
4	2023-01-01 02:00:00	7.6215	0.0	10.691453
...	...	...	...	...
8779	2024-01-01 17:00:00	4.4215	0.0	17.227421
8780	2024-01-01 18:00:00	4.1215	0.0	17.906157
8781	2024-01-01 19:00:00	4.0215	0.0	18.359999
8782	2024-01-01 20:00:00	4.0215	0.0	17.906157
8783	2024-01-01 21:00:00	3.5215	0.0	16.485485

	date	temperature_2m	precipitation	wind_speed_10m	trip_start_count	hour	day_of_week	month	is_weekend_or_holiday	is_strike
count	8760	8760.000000	8760.000000	8760.000000	8760.000000	8760.000000	8760.000000	8760.000000	8760.000000	8760.000000
mean	2023-07-02 11:30:00	10.689720	0.131016	11.269616	23.502169	11.500000	3.008219	6.526027	0.320548	0.010959
min	2023-01-01 00:00:00	-13.628500	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000
25%	2023-04-02 05:45:00	4.621500	0.000000	6.763786	6.000000	5.750000	1.000000	4.000000	0.000000	0.000000
50%	2023-07-02 11:30:00	10.171500	0.000000	9.686609	17.000000	11.500000	3.000000	7.000000	0.000000	0.000000
75%	2023-10-01 17:15:00	16.671501	0.000000	14.118243	34.000000	17.250000	5.000000	10.000000	1.000000	0.000000
max	2023-12-31 23:00:00	33.871498	13.700000	43.850594	224.000000	23.000000	6.000000	12.000000	1.000000	1.000000
std	NaN	8.087231	0.459431	6.596209	22.653266	6.922582	2.003519	3.448048	0.466714	0.104116

	trip_index	STARTTIME	ENDTIME	STARTLAT	STARTLON	ENDLAT	ENDLON	RENTAL_IS_STATION	RENTAL_STATION_NAME	RETURN_IS_STATION	RETURN_STATION_NAME
0	0	2022-01-01 00:02:00	2022-01-01 00:09:00	48.13658	11.59283	48.14159	11.59721	0		NaN
1	1	2022-01-01 00:03:00	2022-01-01 00:07:00	48.13659	11.59282	48.14165	11.59701	0		NaN
2	2	2022-01-01 00:16:00	2022-01-01 00:29:00	48.16849	11.55028	48.15526	11.54012	0		NaN	Albrechtstraße
3	3	2022-01-01 00:16:00	2022-01-01 00:29:00	48.16852	11.55020	48.15526	11.54012	0		NaN	Albrechtstraße
5	5	2022-01-01 00:24:00	2022-01-01 00:35:00	48.16862	11.55234	48.15288	11.56669	0		NaN
...	...	...	...	...	...	...	...	...	...	...	...
709139	709139	2022-12-31 23:38:00	2022-12-31 23:48:00	48.16371	11.57453	48.16843	11.55567	0		NaN
709140	709140	2022-12-31 23:39:00	2023-01-01 00:56:00	48.15855	11.53505	48.17049	11.54825	0		NaN
709141	709141	2022-12-31 23:43:00	2022-12-31 23:57:00	48.15772	11.51174	48.17067	11.54825	0		NaN
709142	709142	2022-12-31 23:48:00	2022-12-31 23:56:00	48.14471	11.61595	48.14085	11.59835	0		NaN
709143	709143	2022-12-31 23:52:00	2023-01-01 16:36:00	47.99317	11.73690	47.99974	11.76324	1	Bahnhof Dürnhaar	NaN

	date	temperature_2m	precipitation	wind_speed_10m
0	2021-12-31 22:00:00	9.221499	0.0	19.011953
1	2021-12-31 23:00:00	9.021500	0.0	19.011953
2	2022-01-01 00:00:00	8.621500	0.0	16.645864
3	2022-01-01 01:00:00	8.271500	0.0	15.111424
4	2022-01-01 02:00:00	7.721500	0.0	13.397612
...	...	...	...	...
8779	2023-01-01 17:00:00	8.771500	0.0	9.085988
8780	2023-01-01 18:00:00	8.771500	0.0	9.511088
8781	2023-01-01 19:00:00	6.971500	0.0	8.311245
8782	2023-01-01 20:00:00	5.521500	0.0	7.342588
8783	2023-01-01 21:00:00	4.921500	0.0	8.825508

	date	temperature_2m	precipitation	wind_speed_10m	trip_start_count	hour	day_of_week	month	is_weekend_or_holiday	is_strike
2	2022-01-01 00:00:00	8.6215	0.0	16.645864	3	0	5	1	1	0
3	2022-01-01 01:00:00	8.2715	0.0	15.111424	9	1	5	1	1	0
4	2022-01-01 02:00:00	7.7215	0.0	13.397612	15	2	5	1	1	0
5	2022-01-01 03:00:00	7.7715	0.0	14.917212	15	3	5	1	1	0
6	2022-01-01 04:00:00	7.8215	0.0	12.574260	8	4	5	1	1	0
...	...	...	...	...	...	...	...	...	...	...
8757	2022-12-31 19:00:00	8.2715	0.0	11.753877	20	19	5	12	1	0
8758	2022-12-31 20:00:00	7.9215	0.0	10.948973	12	20	5	12	1	0
8759	2022-12-31 21:00:00	7.4715	0.0	12.074766	5	21	5	12	1	0
8760	2022-12-31 22:00:00	7.4215	0.0	12.722830	10	22	5	12	1	0
8761	2022-12-31 23:00:00	7.6215	0.0	12.245293	8	23	5	12	1	0

	y_test	y_pred	temperature	precipitation
date
2022-01-01 00:00:00	3	14.561603	8.6215	0.0
2022-01-01 01:00:00	9	16.955082	8.2715	0.0
2022-01-01 02:00:00	15	10.381706	7.7215	0.0
2022-01-01 03:00:00	15	9.806506	7.7715	0.0
2022-01-01 04:00:00	8	5.379688	7.8215	0.0
...	...	...	...	...
2022-12-31 19:00:00	20	9.164827	8.2715	0.0
2022-12-31 20:00:00	12	4.876100	7.9215	0.0
2022-12-31 21:00:00	5	4.277053	7.4715	0.0
2022-12-31 22:00:00	10	4.456097	7.4215	0.0
2022-12-31 23:00:00	8	4.412558	7.6215	0.0